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Real time estimation of impaired aircraft flight envelope using feedforward neural networks

机译：使用前馈神经网络的飞机飞行信封的实时估计

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Extensive research in recent years has focused on developing flight envelope estimation methods to improve current loss of control prevention and recovery systems. Such methods are practically efficient only if they are able to evaluate in real time the new flight envelope of damaged aircraft based on the altered dynamics. Due to nonlinear dynamics of aircraft, common approaches to estimate the entire flight envelope of high-fidelity models are numerically intensive and their real time implementation is computationally impossible. So current methods are based on reduced complexity models or flight envelopes are determined locally. This paper presents a novel method to estimate the global flight envelope of impaired aircraft in real-time for any unknown failure degree. In the proposed method, first, numerous flight envelopes are evaluated using a high fidelity model at various failure degrees and different flight conditions and prepared as training data. Then multiple feedforward neural networks are trained offline by a Bayesian regularization backpropagation algorithm. Finally, the trained networks are used to estimate flight envelopes in real time. The method is applied to rudder and aileron failure cases of the NASA Generic Transport Model. Results show that the estimated flight envelopes are good approximations of the high fidelity global flight envelopes. (C) 2019 Elsevier Masson SAS. All rights reserved.
机译：近年来广泛的研究专注于开发飞行包络估计方法，以改善防治防治恢复系统的目前损失。只有在实时评估基于改变的动态的损坏飞机的新飞行包时，这些方法才是实际有效的。由于飞机的非线性动态，估计的常见方法估计高保真模型的整个飞行包络数是数值密集的，并且其实时实现是计算不可能的。因此，目前的方法基于降低的复杂性模型或飞行信封在本地确定。本文介绍了一种新的方法，可以实时估计受损飞机的全球飞行包络，以进行任何未知的故障程度。在所提出的方法中，首先，在各种故障程度和不同飞行条件下使用高保真模型评估许多飞行信封，并准备好作为训练数据。然后，多馈通向神经网络通过贝叶斯正则化反向算法脱离线路培训。最后，训练有素的网络用于实时估算飞行信封。该方法应用于NASA通用传输模型的舵和Aileron故障情况。结果表明，估计的飞行信封是高保真全球飞行信封的近似值。 （c）2019年Elsevier Masson SAS。版权所有。

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